كاربرد تحليل مؤلفه ي اصلي در تفسير افق هاي لرزه يي

PRINCIPAL COMPONENT ANALYSIS APPLIED TO SEISMIC HORIZON INTERPRETATIONS

One of the most important stages in seismic interpretation is picking especial horizons in order to detect their underground downward and upward movements in an oilfield. Background noise, however, causes many difficulties to this end. Considering a narrow window of a seismic section, whose reflectors are nearly horizontal, and applying a multivariate statistical method called the Principal Component Analysis, we find the largest eigenvalue that has the most contribution to the variance of data. Lower eigenvalues are subject to noise. Projecting data onto an eigenvector associated with the largest eigenvalue, we obtain a trace with sharper peaks and troughs. This method is applied to two synthetic models; horizontal reflectors and anticline. We, also, examine the window length and dominant frequency of the seismic wavelet. Obtained trace with significantly attenuated noise can be used for tracking weak horizons in a seismic section with a signal-to-noise ratio of 0.2. Dominant frequency cannot change the result considerably. Optimum window length is the area in which reflectors are horizontal. It is also applied to the real data of an oilfield in S.W. Iran. The obtained results were useful in picking some important horizons.